In this research, an improved Partitioned Iterated Function System (PIFS) scheme is introduced to speed up the encoding stage time in Fractal Image Compression (FIC). The proposed system is based on using both symmetry prediction and blocks indexing to speed up the block matching process; these two methods have been used to manage the way of selecting the suitable domain to represent the range block. For block indexing the moment features have been used for determining double block descriptors, each one is affine invariant. The two block descriptors are utilized in a combined indexing scheme to classify the blocks of both range and domain pool. So, the block indexing method is introduced to filter the domain blocks, and keeps only those domain blocks have similar block indices with that of the mapped range block which will be approximately represented using affine mapping. The symmetry predictor is used to reduce the number of isometric trails from 8 to one trail .The research also includes the implementation of the use of single moment descriptor to speed up FIC for comparison. The test results indicated that the proposed improvement has reduced the required encoding time to 0.25 second, and the attain compression ratio is 7.99 without making significant degradation in image quality

In this paper we investigate the effect of image size on the compression parameters of the fractal image compression technique(FIC) proposed by Jacquin This research is tested on 8 bits/pixel gray images, three different size of gray image have been used (100 x 100, 150 x 150 ,256 x256). The results show that the PSNR, Bit Rate and Encoding time are increase with the increase of image size, but the CR is decreases with the increase of image size, The quality of the reconstructed image is pleasurable either we use any size of images .

This paper proposed some methods for applying fast fractal image compression (FFIC) on haar wavelet transformed images. The received red, green and blue (RGB) color image is first converted to YCbCr color space, Then Haar wavelet transform is applied to each of the subbands Y, Cb and Cr separately. This produces four smaller filtered images or subbands: LL, HL, LH, and HH. Three m are conducted to test the effect of applying fractal compression on these four subbands. In each method the subbands are treated in different way by applying the FFIC on some parts and leave the others without any changes to find the best compression method. The FFIC is speeded up by using the centralized moment descriptors which are applied on each range and domain block, then sort the domain blocks to determine the suitable symmetry case without trying the eight symmetry cases when searching for the best match in the domain blocks. The subbands (HL, LH, and HH) in Cb and Cr components are not saved at all to increase the compression because these parts do not contain important information that affects the quality of the image while the LL part and all Y component parts are managed in different way in each of the three suggested methods. Quantization is applied to reduce the saved data. Finally the approach is tested on Lena’s images using the PSNR to test the quality, compression ratio and the compression time parameters.

Abstract This work discusses the compression objects ratio for Macromedia Flash File (SWF) Image by Wavelet functions for compression and there effect for Macromedia Flash File (SWF) Images compression . We discusses classification objects in Macromedia Flash (SWF) image in to nine types objects Action, Font, Image, Sound, Text, Button, Frame, Shape and Sprite. The work is particularly targeted towards wavelet image compression best case by using Haar Wavelet Transformation with an idea to minimize the computational requirements by applying different compression thresholds for the wavelet coefficients and these results are obtained in fraction of seconds and thus to improve the quality of the reconstructed image. The promising results obtained concerning reconstructed images quality as well as preservation of significant image details, while, on the other hand achieving high compression rates and better image quality while DB4 Wavelet Transformation higher compression rates ratio without kept for image quality .

Image compression involves reducing the size of image data file, while retaining necessary information.This paper uses the facilities of the Genetic Algorithm for the enhancement of the performance of one of the popular compression method, Vector Quantization method is selected in this work. After studying this method, new proposed algorithm for mixing the Genetic Algorithm with this method was constructed and then the required programs for testing this algorithm was written. The proposed algorithm was tested by applying it on some image data files. Some fidelity measures are calculated to evaluate the performance of the new proposed algorithm. A good enhancement was recorded for the performance of the Vector Quantization method when mixed with the Genetic Algorithm. All programs were written by using Matlab (version 7.0) and these programs were executed on the Pentium III (866 MHz) personal computer.

This paper describes a new lossy image compression decompression algorithm. In lossy compression techniques there are some loss of information, and image cannot be reconstructed exactly.This algorithm will be referred to as (IWDC), which stands for integer wavelet (IWT) and discrete cosine transform (DCT) and this algorithm improves existing techniques and develops new image compressors.(IWDC) is efficient than corresponding DCT and wavelet transform functions and incorporating DCT and integer wavelet transform are shown to improve the performance of the DCT and integer wavelet (IWT). In the new proposed compression is more efficient than the still image compression methods.

Nowadays, still images are usedeverywhere in the digital world. Images takelot of computer space, in many practicalsituations, all original images cannot bestored, and a compression must be used.Moreover, in many such situations,compression ratio provided by even the bestlossless compression is not sufficient, so lossycompression is used.In this paper ,Differential pulse codemodulation (DPCM) in slantlet transformand Run Length Code for image compressionis used.Apply slantlet transform on eachcomponent in the color image(after applyingcolor space conversion from RGB toYCbCr)and encoding Y component by DPCMand encoding Cb and Cr with RLC.Thecompression ratio and Peak Signal to NoiseRatio (PSNR) are used as measurement tools.When comparing the proposed approach withother compression methods Good resultobtained.

Abstract:The paper describe approach to the image compression using new hybrid Transforms ,namely, the improvement ridgelet transform that has proven to show promising results over ridgelet transform. The hybrid transform based of replacing the wavelet transform with the slantlet transform, the slantlet transform is a discrete wavelet transform with two zero moments and with improved time localization. A comparison was made with compression using ridgelet transform for different images. A high quality image compression has been achieved for natural images. Computer simulation results indicate that the improvement ridgelet transform offers superior and faster compression performance compared to the ridgelet transform based approaches.

In this paper, A steady – state Genetic Algorithm (SSGA) based two phase image quantization algorithm for image compression is proposed. The aim of the first phase is create the initial codebook that used to categorize the image blocks by a according to some distortion measures, while the SSGA will produce the best representative Block that represents the all blocks in each unit within the codebook in the second phase. The simulation results explain that the proposed SSGA exhibits a good compression ratio with high quality reconstructed image.

Image compression is very important in reducing the costs of data storage transmission in relatively slow channels. Wavelet transform has received significant attention because their multiresolution decomposition that allows efficient image analysis. This paper attempts to give an understanding of the wavelet transform using two more popular examples for wavelet transform, Haar and Daubechies techniques, and make compression between their effects on the image compression.